Overview

Dataset statistics

Number of variables27
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory310.2 KiB
Average record size in memory216.1 B

Variable types

NUM14
CAT11
BOOL2

Reproduction

Analysis started2020-07-26 14:04:25.002103
Analysis finished2020-07-26 14:08:56.256315
Duration4 minutes and 31.25 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

MonthlyIncome is highly correlated with JobLevelHigh correlation
JobLevel is highly correlated with MonthlyIncomeHigh correlation
JobRole is highly correlated with DepartmentHigh correlation
Department is highly correlated with JobRoleHigh correlation
NumCompaniesWorked has 197 (13.4%) zeros Zeros
TrainingTimesLastYear has 54 (3.7%) zeros Zeros
YearsAtCompany has 44 (3.0%) zeros Zeros
YearsInCurrentRole has 244 (16.6%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithCurrManager has 263 (17.9%) zeros Zeros

Variables

Age
Real number (ℝ≥0)

Distinct count43
Unique (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.923809523809524
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2020-07-26T14:08:56.759330image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.135373489
Coefficient of variation (CV)0.2474114564
Kurtosis-0.4041451372
Mean36.92380952
Median Absolute Deviation (MAD)6
Skewness0.4132863019
Sum54278
Variance83.45504879
2020-07-26T14:08:57.435126image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
35785.3%
 
34775.2%
 
31694.7%
 
36694.7%
 
29684.6%
 
32614.1%
 
30604.1%
 
33583.9%
 
38583.9%
 
40573.9%
 
Other values (33)81555.4%
 
ValueCountFrequency (%) 
1880.5%
 
1990.6%
 
20110.7%
 
21130.9%
 
22161.1%
 
ValueCountFrequency (%) 
6050.3%
 
59100.7%
 
58141.0%
 
5740.3%
 
56141.0%
 

Attrition
Boolean

Distinct count2
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1233
1
 
237
ValueCountFrequency (%) 
0123383.9%
 
123716.1%
 

BusinessTravel
Categorical

Distinct count3
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Travel_Rarely
1043
Travel_Frequently
277
Non-Travel
 
150
ValueCountFrequency (%) 
Travel_Rarely104371.0%
 
Travel_Frequently27718.8%
 
Non-Travel15010.2%
 
2020-07-26T14:08:58.412172image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length17
Median length13
Mean length13.44761905
Min length10

Department
Categorical

HIGH CORRELATION

Distinct count3
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Research & Development
961
Sales
446
Human Resources
 
63
ValueCountFrequency (%) 
Research & Development96165.4%
 
Sales44630.3%
 
Human Resources634.3%
 
2020-07-26T14:08:59.331730image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length16.54217687
Min length5

DistanceFromHome
Real number (ℝ≥0)

Distinct count29
Unique (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.19251700680272
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2020-07-26T14:09:00.071119image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.106864436
Coefficient of variation (CV)0.8818982254
Kurtosis-0.2248334049
Mean9.192517007
Median Absolute Deviation (MAD)5
Skewness0.9581179957
Sum13513
Variance65.72125098
2020-07-26T14:09:00.753210image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
221114.4%
 
120814.1%
 
10865.9%
 
9855.8%
 
3845.7%
 
7845.7%
 
8805.4%
 
5654.4%
 
4644.4%
 
6594.0%
 
Other values (19)44430.2%
 
ValueCountFrequency (%) 
120814.1%
 
221114.4%
 
3845.7%
 
4644.4%
 
5654.4%
 
ValueCountFrequency (%) 
29271.8%
 
28231.6%
 
27120.8%
 
26251.7%
 
25251.7%
 

EducationField
Categorical

Distinct count6
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Life Sciences
606
Medical
464
Marketing
159
Technical Degree
132
Other
 
82
ValueCountFrequency (%) 
Life Sciences60641.2%
 
Medical46431.6%
 
Marketing15910.8%
 
Technical Degree1329.0%
 
Other825.6%
 
Human Resources271.8%
 
2020-07-26T14:09:01.756987image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length16
Median length13
Mean length10.53333333
Min length5
Distinct count4
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
3
453
4
446
2
287
1
284
ValueCountFrequency (%) 
345330.8%
 
444630.3%
 
228719.5%
 
128419.3%
 
2020-07-26T14:09:02.754797image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Gender
Categorical

Distinct count2
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Male
882
Female
588
ValueCountFrequency (%) 
Male88260.0%
 
Female58840.0%
 
2020-07-26T14:09:03.731151image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.8
Min length4

HourlyRate
Real number (ℝ≥0)

Distinct count71
Unique (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.89115646258503
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2020-07-26T14:09:04.521896image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.32942759
Coefficient of variation (CV)0.3085304415
Kurtosis-1.196398456
Mean65.89115646
Median Absolute Deviation (MAD)18
Skewness-0.0323109529
Sum96860
Variance413.2856263
2020-07-26T14:09:05.091462image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
66292.0%
 
42281.9%
 
98281.9%
 
48281.9%
 
84281.9%
 
79271.8%
 
96271.8%
 
57271.8%
 
52261.8%
 
87261.8%
 
Other values (61)119681.4%
 
ValueCountFrequency (%) 
30191.3%
 
31151.0%
 
32241.6%
 
33191.3%
 
34120.8%
 
ValueCountFrequency (%) 
100191.3%
 
99201.4%
 
98281.9%
 
97211.4%
 
96271.8%
 

JobInvolvement
Categorical

Distinct count4
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
3
868
2
375
4
 
144
1
 
83
ValueCountFrequency (%) 
386859.0%
 
237525.5%
 
41449.8%
 
1835.6%
 
2020-07-26T14:09:05.996170image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

JobLevel
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count5
Unique (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0639455782312925
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2020-07-26T14:09:06.834033image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.106939899
Coefficient of variation (CV)0.5363222319
Kurtosis0.3991520554
Mean2.063945578
Median Absolute Deviation (MAD)1
Skewness1.025401283
Sum3034
Variance1.22531594
2020-07-26T14:09:07.520302image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
154336.9%
 
253436.3%
 
321814.8%
 
41067.2%
 
5694.7%
 
ValueCountFrequency (%) 
154336.9%
 
253436.3%
 
321814.8%
 
41067.2%
 
5694.7%
 
ValueCountFrequency (%) 
5694.7%
 
41067.2%
 
321814.8%
 
253436.3%
 
154336.9%
 

JobRole
Categorical

HIGH CORRELATION

Distinct count9
Unique (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Sales Executive
326
Research Scientist
292
Laboratory Technician
259
Manufacturing Director
145
Healthcare Representative
131
Other values (4)
317
ValueCountFrequency (%) 
Sales Executive32622.2%
 
Research Scientist29219.9%
 
Laboratory Technician25917.6%
 
Manufacturing Director1459.9%
 
Healthcare Representative1318.9%
 
Manager1026.9%
 
Sales Representative835.6%
 
Research Director805.4%
 
Human Resources523.5%
 
2020-07-26T14:09:08.501406image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length25
Median length18
Mean length18.0707483
Min length7

JobSatisfaction
Categorical

Distinct count4
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
4
459
3
442
1
289
2
280
ValueCountFrequency (%) 
445931.2%
 
344230.1%
 
128919.7%
 
228019.0%
 
2020-07-26T14:09:09.458707image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

MaritalStatus
Categorical

Distinct count3
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Married
673
Single
470
Divorced
327
ValueCountFrequency (%) 
Married67345.8%
 
Single47032.0%
 
Divorced32722.2%
 
2020-07-26T14:09:10.476423image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.902721088
Min length6

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1349
Unique (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.931292517007
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2020-07-26T14:09:11.224431image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.956783
Coefficient of variation (CV)0.7239745541
Kurtosis1.005232691
Mean6502.931293
Median Absolute Deviation (MAD)2199
Skewness1.369816681
Sum9559309
Variance22164857.07
2020-07-26T14:09:11.878700image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
234240.3%
 
556230.2%
 
274130.2%
 
245130.2%
 
261030.2%
 
238030.2%
 
614230.2%
 
634730.2%
 
255930.2%
 
240430.2%
 
Other values (1339)143997.9%
 
ValueCountFrequency (%) 
100910.1%
 
105110.1%
 
105210.1%
 
108110.1%
 
109110.1%
 
ValueCountFrequency (%) 
1999910.1%
 
1997310.1%
 
1994310.1%
 
1992610.1%
 
1985910.1%
 

MonthlyRate
Real number (ℝ≥0)

Distinct count1427
Unique (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.103401360544
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2020-07-26T14:09:12.665196image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786044
Coefficient of variation (CV)0.4972915967
Kurtosis-1.2149561
Mean14313.1034
Median Absolute Deviation (MAD)6206.5
Skewness0.01857780789
Sum21040262
Variance50662878.17
2020-07-26T14:09:13.404950image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
422330.2%
 
915030.2%
 
667020.1%
 
732420.1%
 
465820.1%
 
2153420.1%
 
1615420.1%
 
1300820.1%
 
1235520.1%
 
606920.1%
 
Other values (1417)144898.5%
 
ValueCountFrequency (%) 
209410.1%
 
209710.1%
 
210410.1%
 
211210.1%
 
212210.1%
 
ValueCountFrequency (%) 
2699910.1%
 
2699710.1%
 
2696810.1%
 
2695910.1%
 
2695610.1%
 

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931972789115646
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Memory size11.5 KiB
2020-07-26T14:09:14.209535image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009006
Coefficient of variation (CV)0.9275254455
Kurtosis0.01021381669
Mean2.693197279
Median Absolute Deviation (MAD)1
Skewness1.026471112
Sum3959
Variance6.240048994
2020-07-26T14:09:14.894128image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
152135.4%
 
019713.4%
 
315910.8%
 
21469.9%
 
41399.5%
 
7745.0%
 
6704.8%
 
5634.3%
 
9523.5%
 
8493.3%
 
ValueCountFrequency (%) 
019713.4%
 
152135.4%
 
21469.9%
 
315910.8%
 
41399.5%
 
ValueCountFrequency (%) 
9523.5%
 
8493.3%
 
7745.0%
 
6704.8%
 
5634.3%
 

OverTime
Boolean

Distinct count2
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
No
1054
Yes
416
ValueCountFrequency (%) 
No105471.7%
 
Yes41628.3%
 

PercentSalaryHike
Real number (ℝ≥0)

Distinct count15
Unique (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209523809523809
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2020-07-26T14:09:15.754533image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.659937717
Coefficient of variation (CV)0.2406346025
Kurtosis-0.3005982221
Mean15.20952381
Median Absolute Deviation (MAD)2
Skewness0.8211279756
Sum22358
Variance13.39514409
2020-07-26T14:09:16.483569image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1121014.3%
 
1320914.2%
 
1420113.7%
 
1219813.5%
 
151016.9%
 
18896.1%
 
17825.6%
 
16785.3%
 
19765.2%
 
22563.8%
 
Other values (5)17011.6%
 
ValueCountFrequency (%) 
1121014.3%
 
1219813.5%
 
1320914.2%
 
1420113.7%
 
151016.9%
 
ValueCountFrequency (%) 
25181.2%
 
24211.4%
 
23281.9%
 
22563.8%
 
21483.3%
 
Distinct count2
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
3
1244
4
 
226
ValueCountFrequency (%) 
3124484.6%
 
422615.4%
 
2020-07-26T14:09:17.510725image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

TotalWorkingYears
Real number (ℝ≥0)

Distinct count40
Unique (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279591836734694
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Memory size11.5 KiB
2020-07-26T14:09:18.379795image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.780781676
Coefficient of variation (CV)0.6898105701
Kurtosis0.9182695366
Mean11.27959184
Median Absolute Deviation (MAD)4
Skewness1.117171853
Sum16581
Variance60.54056348
2020-07-26T14:09:21.511183image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1020213.7%
 
61258.5%
 
81037.0%
 
9966.5%
 
5886.0%
 
1815.5%
 
7815.5%
 
4634.3%
 
12483.3%
 
3422.9%
 
Other values (30)54136.8%
 
ValueCountFrequency (%) 
0110.7%
 
1815.5%
 
2312.1%
 
3422.9%
 
4634.3%
 
ValueCountFrequency (%) 
4020.1%
 
3810.1%
 
3740.3%
 
3660.4%
 
3530.2%
 

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197278911564
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Memory size11.5 KiB
2020-07-26T14:09:22.264124image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.289270621
Coefficient of variation (CV)0.4605656896
Kurtosis0.494992986
Mean2.799319728
Median Absolute Deviation (MAD)1
Skewness0.5531241711
Sum4115
Variance1.662218734
2020-07-26T14:09:22.924484image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
254737.2%
 
349133.4%
 
41238.4%
 
51198.1%
 
1714.8%
 
6654.4%
 
0543.7%
 
ValueCountFrequency (%) 
0543.7%
 
1714.8%
 
254737.2%
 
349133.4%
 
41238.4%
 
ValueCountFrequency (%) 
6654.4%
 
51198.1%
 
41238.4%
 
349133.4%
 
254737.2%
 

WorkLifeBalance
Categorical

Distinct count4
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
3
893
2
344
4
 
153
1
 
80
ValueCountFrequency (%) 
389360.7%
 
234423.4%
 
415310.4%
 
1805.4%
 
2020-07-26T14:09:23.899741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

YearsAtCompany
Real number (ℝ≥0)

ZEROS

Distinct count37
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081632653061225
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Memory size11.5 KiB
2020-07-26T14:09:24.756327image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.126525152
Coefficient of variation (CV)0.8741984056
Kurtosis3.935508756
Mean7.008163265
Median Absolute Deviation (MAD)3
Skewness1.764529454
Sum10302
Variance37.53431044
2020-07-26T14:09:25.439185image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
519613.3%
 
117111.6%
 
31288.7%
 
21278.6%
 
101208.2%
 
41107.5%
 
7906.1%
 
9825.6%
 
8805.4%
 
6765.2%
 
Other values (27)29019.7%
 
ValueCountFrequency (%) 
0443.0%
 
117111.6%
 
21278.6%
 
31288.7%
 
41107.5%
 
ValueCountFrequency (%) 
4010.1%
 
3710.1%
 
3620.1%
 
3410.1%
 
3350.3%
 

YearsInCurrentRole
Real number (ℝ≥0)

ZEROS

Distinct count19
Unique (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.229251700680272
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Memory size11.5 KiB
2020-07-26T14:09:26.243791image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137035
Coefficient of variation (CV)0.856685128
Kurtosis0.4774207735
Mean4.229251701
Median Absolute Deviation (MAD)3
Skewness0.9173631563
Sum6217
Variance13.12712197
2020-07-26T14:09:26.905660image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
237225.3%
 
024416.6%
 
722215.1%
 
31359.2%
 
41047.1%
 
8896.1%
 
9674.6%
 
1573.9%
 
6372.5%
 
5362.4%
 
Other values (9)1077.3%
 
ValueCountFrequency (%) 
024416.6%
 
1573.9%
 
237225.3%
 
31359.2%
 
41047.1%
 
ValueCountFrequency (%) 
1820.1%
 
1740.3%
 
1670.5%
 
1580.5%
 
14110.7%
 

YearsSinceLastPromotion
Real number (ℝ≥0)

ZEROS

Distinct count16
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551020408164
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Memory size11.5 KiB
2020-07-26T14:09:27.688854image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.222430279
Coefficient of variation (CV)1.472939213
Kurtosis3.612673115
Mean2.187755102
Median Absolute Deviation (MAD)1
Skewness1.984289983
Sum3216
Variance10.3840569
2020-07-26T14:09:28.494490image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
058139.5%
 
135724.3%
 
215910.8%
 
7765.2%
 
4614.1%
 
3523.5%
 
5453.1%
 
6322.2%
 
11241.6%
 
8181.2%
 
Other values (6)654.4%
 
ValueCountFrequency (%) 
058139.5%
 
135724.3%
 
215910.8%
 
3523.5%
 
4614.1%
 
ValueCountFrequency (%) 
15130.9%
 
1490.6%
 
13100.7%
 
12100.7%
 
11241.6%
 

YearsWithCurrManager
Real number (ℝ≥0)

ZEROS

Distinct count18
Unique (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.12312925170068
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Memory size11.5 KiB
2020-07-26T14:09:29.364787image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.568136121
Coefficient of variation (CV)0.8653951654
Kurtosis0.1710580839
Mean4.123129252
Median Absolute Deviation (MAD)3
Skewness0.833450992
Sum6061
Variance12.73159537
2020-07-26T14:09:30.057798image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
234423.4%
 
026317.9%
 
721614.7%
 
31429.7%
 
81077.3%
 
4986.7%
 
1765.2%
 
9644.4%
 
5312.1%
 
6292.0%
 
Other values (8)1006.8%
 
ValueCountFrequency (%) 
026317.9%
 
1765.2%
 
234423.4%
 
31429.7%
 
4986.7%
 
ValueCountFrequency (%) 
1770.5%
 
1620.1%
 
1550.3%
 
1450.3%
 
13141.0%
 

Interactions

2020-07-26T14:04:59.207741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:00.148763image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:01.125091image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:02.106551image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:03.102124image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:04.093594image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:04.993664image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:05.885297image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:06.839560image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:07.842484image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:08.827082image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:09.868643image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:10.906502image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:11.888922image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:12.938599image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:13.949428image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:15.001321image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:16.120339image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:17.250592image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:18.346009image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:19.348840image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:20.355036image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:21.383592image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:22.412431image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:23.398001image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-26T14:05:25.469616image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:26.539520image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:27.664166image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:28.706358image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:29.781381image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:30.928423image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:32.131997image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:33.261872image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:34.285901image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:35.590041image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:36.715333image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:37.944582image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:39.070442image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:40.236084image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:41.680068image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:05:42.805814image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-26T14:05:57.362612image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-26T14:06:17.789013image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:18.955970image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:20.064564image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:21.247556image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:22.344390image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:23.385026image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:24.470847image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:25.283194image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:26.200168image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-26T14:06:28.199664image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-26T14:06:35.268548image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-26T14:06:46.203467image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:47.240607image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:48.215138image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:06:49.260802image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-26T14:06:53.287898image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-26T14:08:00.795253image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:01.796624image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:02.814568image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:05.759747image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:06.921567image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:08.131201image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:09.325575image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:10.525156image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:11.671438image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:12.707546image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:13.825372image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:14.918252image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:16.051234image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:17.224508image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:18.253614image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:19.319902image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:20.442462image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:21.610456image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:22.690012image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:23.862267image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:24.997238image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:26.143350image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:27.311869image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:28.402302image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:29.546381image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:30.686146image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:31.898384image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:33.360157image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:34.529133image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:35.575583image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:36.699102image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:37.778660image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:38.889658image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:39.913492image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:41.003897image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:42.175819image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-07-26T14:09:31.008601image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-07-26T14:09:33.873234image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-07-26T14:09:36.956661image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-07-26T14:09:39.999982image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-07-26T14:09:43.215275image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-07-26T14:08:44.646868image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T14:08:54.458196image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

AgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationFieldEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
0411Travel_RarelySales1Life Sciences2Female9432Sales Executive4Single5993194798Yes1138016405
1490Travel_FrequentlyResearch & Development8Life Sciences3Male6122Research Scientist2Married5130249071No234103310717
2371Travel_RarelyResearch & Development2Other4Male9221Laboratory Technician3Single209023966Yes1537330000
3330Travel_FrequentlyResearch & Development3Life Sciences4Female5631Research Scientist3Married2909231591Yes1138338730
4270Travel_RarelyResearch & Development2Medical1Male4031Laboratory Technician2Married3468166329No1236332222
5320Travel_FrequentlyResearch & Development2Life Sciences4Male7931Laboratory Technician4Single3068118640No1338227736
6590Travel_RarelyResearch & Development3Medical3Female8141Laboratory Technician1Married267099644Yes20412321000
7300Travel_RarelyResearch & Development24Life Sciences4Male6731Laboratory Technician3Divorced2693133351No2241231000
8380Travel_FrequentlyResearch & Development23Life Sciences4Male4423Manufacturing Director3Single952687870No21410239718
9360Travel_RarelyResearch & Development27Medical3Male9432Healthcare Representative3Married5237165776No13317327777

Last rows

AgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationFieldEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
1460290Travel_RarelyResearch & Development28Medical4Female7321Research Scientist1Single378584891No1435315404
1461501Travel_RarelySales28Marketing4Male3923Sales Executive1Divorced10854165864Yes13320333220
1462390Travel_RarelySales24Marketing2Female6024Sales Executive4Married1203188280No113212220996
1463310Non-TravelResearch & Development5Medical2Male7432Manufacturing Director1Single993637870No19310239417
1464260Travel_RarelySales5Other4Female3021Sales Representative3Single2966213780No1835234200
1465360Travel_FrequentlyResearch & Development23Medical3Male4142Laboratory Technician4Married2571122904No17317335203
1466390Travel_RarelyResearch & Development6Medical4Male4223Healthcare Representative1Married9991214574No1539537717
1467270Travel_RarelyResearch & Development4Life Sciences2Male8742Manufacturing Director2Married614251741Yes2046036203
1468490Travel_FrequentlySales2Medical4Male6322Sales Executive2Married5390132432No14317329608
1469340Travel_RarelyResearch & Development8Medical2Male8242Laboratory Technician3Married4404102282No1236344312